Abstract
We present the first bioluminescence tomography algorithm that makes use of the PDE-constrained concept, which has shown to lead to significant savings in computation times in similar applications. Implementing a sequential quadratic programming (SQP) method, we solve the forward and inverse problems simultaneously. Using numerical results we show that the PDE-constrained SQP approach leads to ~10 fold increase in convergence when compared to a standard unconstrained method.
© 2012 Optical Society of America
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